Quantum-Enhanced Support Vector Machine for Sentiment Classification

Quantum computers have potential computational abilities such as speeding up complex computations, parallelism by superpositions, and handling large data sets. Moreover, the field of natural language processing (NLP) is rapidly attracting researchers and engineers in order to build larger model computations of NLP. Thus, the use of quantum technology in NLP tasks, especially sentiment classification, has the potential to be developed. In this research, we investigate the best technique to represent sentiment sentences so that sentiment can be analyzed using the Quantum-Enhanced Support Vector Machine (QE-SVM) algorithm. Investigations were carried out using circuit parameter optimization methods and data transformation. The pipeline of the proposed method consists of sentence-to-circuit conversion, circuit parameter training, state vector formation, and finally the training and testing processes. As a result, we obtained the best classification results with an accuracy of 93.33% using the SPSA optimization method and PCA transformation data. These results have also outperformed the baseline SVM method.